使用 groupby 进行 OLS 回归 [英] OLS Regression with groupby

问题描述

我想使用 pandas 和 groupby 运行 OLS 回归.

I want to run an OLS regression using pandas and a groupby.

我正在尝试以下代码:

import pandas as pd
from pandas.stats.api import ols

df=pd.read_csv(r'F:\File.csv')
result=df.groupby(['FID']).apply(lambda x: ols(y=df[x['MEAN']], x=df[x['Accum_Prcp'],x['Accum_HDD']]))
print result

但这会返回:

File "C:\Users\spotter\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\indexing.py", line 1150, in _convert_to_indexer
    raise KeyError('%s not in index' % objarr[mask])

    KeyError: '[ 0.84978328  0.72115778  0.53965104  0.52955655  0.73372541  0.64617074\n  0.60040938  0.7147218   0.65533535  0.57980322  0.57382068  0.56543435\n  0.70740831  0.9245337   0.54859569  0.6789395   0.7086157   0.3835853\n  0.54924104  0.80813778  0.83758118  0.22673391  0.26594087  0.63650468\n  0.89889911  0.38324657  0.30235986  0.62922678  0.55219822  0.55950705\n  0.71137557  0.53631811  0.70158798  0.87116361  0.93751381  0.91125518\n  0.80020908  0.75301262  0.82391046  0.77483673  0.63069573  0.44954455\n  0.83578862  0.56338649  0.64236039  0.93270243  0.93077291  0.83847668\n  0.8268959   0.85400317  0.74319769  0.94803537  0.97484929  0.45366017\n  0.80823694  0.82028051  0.63960395  0.63015722  0.73132888  0.55570184\n  0.83265402  0.75009687  0.58207032  0.92064804  0.91058008  0.86726397\n  0.89204098  0.95573514  0.75704367  0.80786363  0.87448548  0.7553715\n  0.88965962  0.82828493  0.82423891  0.81034742  0.90104876  0.78875473\n  0.97369268] not in index'

我的语法有什么不正确的地方吗?

is there something with my syntax that is incorrect?

在没有 groupby 的情况下执行此操作将是这样的:

to do this without a groupby would be something like this:

result = ols(y=df['MEAN'], x=df[['Accum_HDD','Accum_Prcp']])

它可以正常工作.

我的数据框看起来像这样:

My dataframe looks like something like this:

FID  Image_Date   MEAN  Accum_Prcp   Accum_HDD
1     19920506     2.0   500.0        1000.0
1     19930506     1.7   450.0        1050.0
2     19920506     2.7   456.0        992.0
2     19930506     1.9   376.0        800.0 

推荐答案

尝试:

grps=df.groupby(['FID'])
for fid, grp in grps:
    ols(y=grp.loc[:, 'MEAN'], x=grp.loc[:, ['Accum_Prcp', 'Accum_HDD']])

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